Object hiding attacks. There are currently 2 classes of Object Hiding Attacks. The first type is when the adversarial operations are performed on the physical (target) object itself. One proposed method [1]} is to place adversarial objects on top of a target vehicle that evade point-cloud based object detectors, achieving attack success rate of 80%. The adversarial objects can be generated in a white-box or a black-box method. For the white-box attack, the adversarial object is generated using a gradient-based approach to minimize the confidence score of the target object (vehicle); whereas the black-box attack chooses adversarial objects using a genetic algorithm approach to iterate and improve on adversarial object meshes. Cao et al. in [2]} proposed using an optimization-based approach to generate adversarial objects that evade detection by 3D object detectors. In [3]}, the authors exploited the weakness of deep neural-networks (DNN) based detectors and proposed an optimization-based method to generate physically realizable adversarial objects that target multi-sensor fusion, effectively evading both Camera and LiDAR based detectors.
